Bibliographic record
Abstract
Horizontal displays are emerging as a standard platform for engaging participants in collaborative tasks. Little is known about how groups of people view visualizations in these collaborative settings. Several techniques have been proposed to assist, such as duplicating or reorienting the visual displays. However, when visualizations compete for pixels on the display, prior solutions do not work effectively. We first ran an experiment to identify whether orientation on horizontal displays impacts the legibility of simple visualizations such as charts. The results reveal that users are best at reading a chart when it is the right side up, taking them 20% less time to read than when it is upside down. This insight led us to develop the Orientation Agnostic Graph (OA-Graph), making use of a radial layout designed to be legible regardless of orientation. In a second experiment we found that users can read OA-Graphs better than when the graphs are upside down but less well than traditional graphs in the right side up. The design of our novel visualization, informed by radial visualization methods will assist designers in developing charts that are not easily affected by user orientation, an issue that is prevalent in collaborative table-top systems. Certain tasks such as observing relative differences can benefit from OA-Graphs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".